diff --git a/test/Microsoft.ML.Functional.Tests/Validation.cs b/test/Microsoft.ML.Functional.Tests/Validation.cs
index b04eff387a..c1d98881c2 100644
--- a/test/Microsoft.ML.Functional.Tests/Validation.cs
+++ b/test/Microsoft.ML.Functional.Tests/Validation.cs
@@ -3,11 +3,14 @@
 // See the LICENSE file in the project root for more information.
 
 using Microsoft.Data.DataView;
+using Microsoft.ML.Core.Data;
 using Microsoft.ML.Data;
+using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework;
 using Microsoft.ML.Trainers.HalLearners;
 using Xunit;
+using static Microsoft.ML.RunTests.TestDataViewBase;
 
 namespace Microsoft.ML.Functional.Tests
 {
@@ -23,20 +26,20 @@ public class ValidationScenarios
         [Fact]
         void CrossValidation()
         {
-            var mlContext = new MLContext(seed: 789);
+            var mlContext = new MLContext(seed: 1, conc: 1);
 
-            // Get the dataset
+            // Get the dataset.
             var data = mlContext.Data.CreateTextLoader(TestDatasets.housing.GetLoaderColumns(), hasHeader: true)
                 .Read(BaseTestClass.GetDataPath(TestDatasets.housing.trainFilename));
 
-            // Create a pipeline to train on the sentiment data
+            // Create a pipeline to train on the sentiment data.
             var pipeline = mlContext.Transforms.Concatenate("Features", new string[] {
                     "CrimesPerCapita", "PercentResidental", "PercentNonRetail", "CharlesRiver", "NitricOxides", "RoomsPerDwelling",
                     "PercentPre40s", "EmploymentDistance", "HighwayDistance", "TaxRate", "TeacherRatio"})
                 .Append(mlContext.Transforms.CopyColumns("Label", "MedianHomeValue"))
                 .Append(mlContext.Regression.Trainers.OrdinaryLeastSquares());
 
-            // Compute the CV result
+            // Compute the CV result.
             var cvResult = mlContext.Regression.CrossValidate(data, pipeline, numFolds: 5);
 
             // Check that the results are valid
@@ -45,9 +48,58 @@ void CrossValidation()
             Assert.True(cvResult[0].ScoredHoldOutSet is IDataView);
             Assert.Equal(5, cvResult.Length);
 
-            // And validate the metrics
+            // And validate the metrics.
             foreach (var result in cvResult)
                 Common.CheckMetrics(result.Metrics);
         }
+
+        /// <summary>
+        /// Train with validation set.
+        /// </summary>
+        [Fact]
+        public void TrainWithValidationSet()
+        {
+            var mlContext = new MLContext(seed: 1, conc: 1);
+
+            // Get the dataset.
+            var data = mlContext.Data.CreateTextLoader(TestDatasets.housing.GetLoaderColumns(), hasHeader: true)
+                .Read(BaseTestClass.GetDataPath(TestDatasets.housing.trainFilename));
+            var dataSplit = mlContext.Regression.TrainTestSplit(data, testFraction: 0.2);
+            var trainData = dataSplit.TrainSet;
+            var validData = dataSplit.TestSet;
+
+            // Create a pipeline to featurize the dataset.
+            var pipeline = mlContext.Transforms.Concatenate("Features", new string[] {
+                    "CrimesPerCapita", "PercentResidental", "PercentNonRetail", "CharlesRiver", "NitricOxides", "RoomsPerDwelling",
+                    "PercentPre40s", "EmploymentDistance", "HighwayDistance", "TaxRate", "TeacherRatio"})
+                .Append(mlContext.Transforms.CopyColumns("Label", "MedianHomeValue"))
+                .AppendCacheCheckpoint(mlContext) as IEstimator<ITransformer>;
+
+            // Preprocess the datasets.
+            var preprocessor = pipeline.Fit(trainData);
+            var preprocessedTrainData = preprocessor.Transform(trainData);
+            var preprocessedValidData = preprocessor.Transform(validData);
+
+            // Train the model with a validation set.
+            var trainedModel = mlContext.Regression.Trainers.FastTree(new Trainers.FastTree.FastTreeRegressionTrainer.Options {
+                    NumTrees = 2,
+                    EarlyStoppingMetrics = 2,
+                    EarlyStoppingRule = new GLEarlyStoppingCriterion.Arguments()
+                })
+                .Train(trainData: preprocessedTrainData, validationData: preprocessedValidData);
+
+            // Combine the model.
+            var model = preprocessor.Append(trainedModel);
+
+            // Score the data sets.
+            var scoredTrainData = model.Transform(trainData);
+            var scoredValidData = model.Transform(validData);
+
+            var trainMetrics = mlContext.Regression.Evaluate(scoredTrainData);
+            var validMetrics = mlContext.Regression.Evaluate(scoredValidData);
+
+            Common.CheckMetrics(trainMetrics);
+            Common.CheckMetrics(validMetrics);
+        }
     }
 }
diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainWithValidationSet.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainWithValidationSet.cs
deleted file mode 100644
index 9fde458562..0000000000
--- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/TrainWithValidationSet.cs
+++ /dev/null
@@ -1,35 +0,0 @@
-// Licensed to the .NET Foundation under one or more agreements.
-// The .NET Foundation licenses this file to you under the MIT license.
-// See the LICENSE file in the project root for more information.
-
-using Microsoft.ML.RunTests;
-using Xunit;
-
-namespace Microsoft.ML.Tests.Scenarios.Api
-{
-    public partial class ApiScenariosTests
-    {
-        /// <summary>
-        /// Train with validation set: Similar to the simple train scenario, but also support a validation set.
-        /// The learner might be trees with early stopping.
-        /// </summary>
-        [Fact]
-        public void TrainWithValidationSet()
-        {
-            var ml = new MLContext(seed: 1, conc: 1);
-            // Pipeline.
-            var data = ml.Data.ReadFromTextFile<SentimentData>(GetDataPath(TestDatasets.Sentiment.trainFilename), hasHeader: true);
-            var pipeline = ml.Transforms.Text.FeaturizeText("Features", "SentimentText");
-
-            // Train the pipeline, prepare train and validation set.
-            var preprocess = pipeline.Fit(data);
-            var trainData = preprocess.Transform(data);
-            var validDataSource = ml.Data.ReadFromTextFile<SentimentData>(GetDataPath(TestDatasets.Sentiment.testFilename), hasHeader: true);
-            var validData = preprocess.Transform(validDataSource);
-
-            // Train model with validation set.
-            var trainer = ml.BinaryClassification.Trainers.FastTree("Label","Features");
-            var model = trainer.Train(ml.Data.Cache(trainData), ml.Data.Cache(validData));
-        }
-    }
-}