address comments

Author: zjffdu

docs/ml-features.md

@@ -1232,7 +1232,7 @@ lInfNormData = normalizer.transform(dataFrame, {normalizer.p: float("inf")})
 * `withStd`: True by default. Scales the data to unit standard deviation.
 * `withMean`: False by default. Centers the data with mean before scaling. It will build a dense output, so this does not work on sparse input and will raise an exception.
 
-`StandardScaler` is a `Estimator` which can be `fit` on a dataset to produce a `StandardScalerModel`; this amounts to computing summary statistics.  The model can then transform a `Vector` column in a dataset to have unit standard deviation and/or zero mean features.
+`StandardScaler` is an `Estimator` which can be `fit` on a dataset to produce a `StandardScalerModel`; this amounts to computing summary statistics.  The model can then transform a `Vector` column in a dataset to have unit standard deviation and/or zero mean features.
 
 Note that if the standard deviation of a feature is zero, it will return default `0.0` value in the `Vector` for that feature.