diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index fcdc29e69b1d..cee7dc788851 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -1245,10 +1245,14 @@ class GeneralizedLinearRegression(JavaEstimator, HasLabelCol, HasFeaturesCol, Ha
     predictor (link function) and a description of the error distribution (family). It supports
     "gaussian", "binomial", "poisson" and "gamma" as family. Valid link functions for each family
     is listed below. The first link function of each family is the default one.
-    - "gaussian" -> "identity", "log", "inverse"
-    - "binomial" -> "logit", "probit", "cloglog"
-    - "poisson"  -> "log", "identity", "sqrt"
-    - "gamma"    -> "inverse", "identity", "log"
+
+    * "gaussian" -> "identity", "log", "inverse"
+
+    * "binomial" -> "logit", "probit", "cloglog"
+
+    * "poisson"  -> "log", "identity", "sqrt"
+
+    * "gamma"    -> "inverse", "identity", "log"
 
     .. seealso:: `GLM <https://en.wikipedia.org/wiki/Generalized_linear_model>`_
 
@@ -1258,9 +1262,12 @@ class GeneralizedLinearRegression(JavaEstimator, HasLabelCol, HasFeaturesCol, Ha
     ...     (1.0, Vectors.dense(1.0, 2.0)),
     ...     (2.0, Vectors.dense(0.0, 0.0)),
     ...     (2.0, Vectors.dense(1.0, 1.0)),], ["label", "features"])
-    >>> glr = GeneralizedLinearRegression(family="gaussian", link="identity")
+    >>> glr = GeneralizedLinearRegression(family="gaussian", link="identity", linkPredictionCol="p")
     >>> model = glr.fit(df)
-    >>> abs(model.transform(df).head().prediction - 1.5) < 0.001
+    >>> transformed = model.transform(df)
+    >>> abs(transformed.head().prediction - 1.5) < 0.001
+    True
+    >>> abs(transformed.head().p - 1.5) < 0.001
     True
     >>> model.coefficients
     DenseVector([1.5..., -1.0...])
@@ -1290,20 +1297,23 @@ class GeneralizedLinearRegression(JavaEstimator, HasLabelCol, HasFeaturesCol, Ha
                  "relationship between the linear predictor and the mean of the distribution " +
                  "function. Supported options: identity, log, inverse, logit, probit, cloglog " +
                  "and sqrt.", typeConverter=TypeConverters.toString)
+    linkPredictionCol = Param(Params._dummy(), "linkPredictionCol", "link prediction (linear " +
+                              "predictor) column name", typeConverter=TypeConverters.toString)
 
     @keyword_only
     def __init__(self, labelCol="label", featuresCol="features", predictionCol="prediction",
                  family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6,
-                 regParam=0.0, weightCol=None, solver="irls"):
+                 regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=""):
         """
         __init__(self, labelCol="label", featuresCol="features", predictionCol="prediction", \
                  family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6, \
-                 regParam=0.0, weightCol=None, solver="irls")
+                 regParam=0.0, weightCol=None, solver="irls", linkPredictionCol="")
         """
         super(GeneralizedLinearRegression, self).__init__()
         self._java_obj = self._new_java_obj(
             "org.apache.spark.ml.regression.GeneralizedLinearRegression", self.uid)
-        self._setDefault(family="gaussian", maxIter=25, tol=1e-6, regParam=0.0, solver="irls")
+        self._setDefault(family="gaussian", maxIter=25, tol=1e-6, regParam=0.0, solver="irls",
+                         linkPredictionCol="")
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
@@ -1311,11 +1321,11 @@ def __init__(self, labelCol="label", featuresCol="features", predictionCol="pred
     @since("2.0.0")
     def setParams(self, labelCol="label", featuresCol="features", predictionCol="prediction",
                   family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6,
-                  regParam=0.0, weightCol=None, solver="irls"):
+                  regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=""):
         """
         setParams(self, labelCol="label", featuresCol="features", predictionCol="prediction", \
                   family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6, \
-                  regParam=0.0, weightCol=None, solver="irls")
+                  regParam=0.0, weightCol=None, solver="irls", linkPredictionCol="")
         Sets params for generalized linear regression.
         """
         kwargs = self.setParams._input_kwargs
@@ -1338,6 +1348,20 @@ def getFamily(self):
         """
         return self.getOrDefault(self.family)
 
+    @since("2.0.0")
+    def setLinkPredictionCol(self, value):
+        """
+        Sets the value of :py:attr:`linkPredictionCol`.
+        """
+        return self._set(linkPredictionCol=value)
+
+    @since("2.0.0")
+    def getLinkPredictionCol(self):
+        """
+        Gets the value of linkPredictionCol or its default value.
+        """
+        return self.getOrDefault(self.linkPredictionCol)
+
     @since("2.0.0")
     def setLink(self, value):
         """