diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
index 1e04c37fca53e..28e3727f2c064 100644
--- a/python/pyspark/ml/tuning.py
+++ b/python/pyspark/ml/tuning.py
@@ -27,24 +27,22 @@
 
 
 class ParamGridBuilder(object):
-    """
+    r"""
     Builder for a param grid used in grid search-based model selection.
 
-    >>> from classification import LogisticRegression
+    >>> from pyspark.ml.classification import LogisticRegression
     >>> lr = LogisticRegression()
-    >>> output = ParamGridBuilder().baseOn({lr.labelCol: 'l'}) \
-            .baseOn([lr.predictionCol, 'p']) \
-            .addGrid(lr.regParam, [1.0, 2.0, 3.0]) \
-            .addGrid(lr.maxIter, [1, 5]) \
-            .addGrid(lr.featuresCol, ['f']) \
-            .build()
-    >>> expected = [ \
-{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
-{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
-{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
-{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
-{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
-{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}]
+    >>> output = ParamGridBuilder() \
+    ...     .baseOn({lr.labelCol: 'l'}) \
+    ...     .baseOn([lr.predictionCol, 'p']) \
+    ...     .addGrid(lr.regParam, [1.0, 2.0]) \
+    ...     .addGrid(lr.maxIter, [1, 5]) \
+    ...     .build()
+    >>> expected = [
+    ...     {lr.regParam: 1.0, lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'},
+    ...     {lr.regParam: 2.0, lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'},
+    ...     {lr.regParam: 1.0, lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'},
+    ...     {lr.regParam: 2.0, lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}]
     >>> len(output) == len(expected)
     True
     >>> all([m in expected for m in output])