diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index 5c0d3dcf3af9..a50ff5f12563 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -29,15 +29,10 @@ setClass("PipelineModel", representation(model = "jobj"))
 #' @param formula A symbolic description of the model to be fitted. Currently only a few formula
 #'                operators are supported, including '~', '.', ':', '+', and '-'.
 #' @param data DataFrame for training
-#' @param family Error distribution. "gaussian" -> linear regression, "binomial" -> logistic reg.
+#' @param family a description of the error distribution and link function to be used in the model,
+#'               as in [[https://stat.ethz.ch/R-manual/R-devel/library/stats/html/family.html]]
 #' @param lambda Regularization parameter
-#' @param alpha Elastic-net mixing parameter (see glmnet's documentation for details)
-#' @param standardize Whether to standardize features before training
-#' @param solver The solver algorithm used for optimization, this can be "l-bfgs", "normal" and
-#'               "auto". "l-bfgs" denotes Limited-memory BFGS which is a limited-memory
-#'               quasi-Newton optimization method. "normal" denotes using Normal Equation as an
-#'               analytical solution to the linear regression problem. The default value is "auto"
-#'               which means that the solver algorithm is selected automatically.
+#' @param solver Currently only support "irls" which is also the default solver.
 #' @return a fitted MLlib model
 #' @rdname glm
 #' @export
@@ -51,13 +46,12 @@ setClass("PipelineModel", representation(model = "jobj"))
 #' summary(model)
 #'}
 setMethod("glm", signature(formula = "formula", family = "ANY", data = "DataFrame"),
-          function(formula, family = c("gaussian", "binomial"), data, lambda = 0, alpha = 0,
-            standardize = TRUE, solver = "auto") {
-            family <- match.arg(family)
+          function(formula, family = gaussian(), data, lambda = 0, solver = "auto") {
+            familyName <- family$family
+            linkName <- family$link
             formula <- paste(deparse(formula), collapse = "")
             model <- callJStatic("org.apache.spark.ml.api.r.SparkRWrappers",
-                                 "fitRModelFormula", formula, data@sdf, family, lambda,
-                                 alpha, standardize, solver)
+                                 "fitGLM", formula, data@sdf, familyName, linkName, lambda, solver)
             return(new("PipelineModel", model = model))
           })
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala b/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala
index d23e4fc9d1f5..84cb02d19b7a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/r/SparkRWrappers.scala
@@ -17,15 +17,34 @@
 
 package org.apache.spark.ml.api.r
 
+import org.apache.spark.SparkException
 import org.apache.spark.ml.{Pipeline, PipelineModel}
 import org.apache.spark.ml.attribute._
 import org.apache.spark.ml.classification.{LogisticRegression, LogisticRegressionModel}
 import org.apache.spark.ml.clustering.{KMeans, KMeansModel}
 import org.apache.spark.ml.feature.{RFormula, VectorAssembler}
-import org.apache.spark.ml.regression.{LinearRegression, LinearRegressionModel}
+import org.apache.spark.ml.regression._
 import org.apache.spark.sql.DataFrame
 
 private[r] object SparkRWrappers {
+  def fitGLM(
+      value: String,
+      df: DataFrame,
+      family: String,
+      link: String,
+      lambda: Double,
+      solver: String): PipelineModel = {
+    val formula = new RFormula().setFormula(value)
+    val estimator = new GeneralizedLinearRegression()
+      .setFamily(family)
+      .setRegParam(lambda)
+      .setFitIntercept(formula.hasIntercept)
+
+    if (link != null) estimator.setLink(link)
+    val pipeline = new Pipeline().setStages(Array(formula, estimator))
+    pipeline.fit(df)
+  }
+
   def fitRModelFormula(
       value: String,
       df: DataFrame,