diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index 9a66d87d7f21..8521fd297240 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -95,6 +95,7 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
     True
     >>> model.numFeatures
     1
+    >>> model.write().format("pmml").save(model_path + "_2")
 
     .. versionadded:: 1.4.0
     """
@@ -161,7 +162,7 @@ def getEpsilon(self):
         return self.getOrDefault(self.epsilon)
 
 
-class LinearRegressionModel(JavaModel, JavaPredictionModel, JavaMLWritable, JavaMLReadable):
+class LinearRegressionModel(JavaModel, JavaPredictionModel, GeneralJavaMLWritable, JavaMLReadable):
     """
     Model fitted by :class:`LinearRegression`.
 
diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index 2ec0be60e9fa..0869b6fd59f1 100755
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -1355,6 +1355,23 @@ def test_linear_regression(self):
         except OSError:
             pass
 
+    def test_linear_regression_pmml_basic(self):
+        # Most of the validation is done in the Scala side, here we just check
+        # that we output text rather than parquet (e.g. that the format flag
+        # was respected).
+        df = self.spark.createDataFrame([(1.0, 2.0, Vectors.dense(1.0)),
+                                         (0.0, 2.0, Vectors.sparse(1, [], []))],
+                                        ["label", "weight", "features"])
+        lr = LinearRegression(maxIter=1)
+        model = lr.fit(df)
+        path = tempfile.mkdtemp()
+        lr_path = path + "/lr-pmml"
+        model.write().format("pmml").save(lr_path)
+        pmml_text_list = self.sc.textFile(lr_path).collect()
+        pmml_text = "\n".join(pmml_text_list)
+        self.assertIn("Apache Spark", pmml_text)
+        self.assertIn("PMML", pmml_text)
+
     def test_logistic_regression(self):
         lr = LogisticRegression(maxIter=1)
         path = tempfile.mkdtemp()
diff --git a/python/pyspark/ml/util.py b/python/pyspark/ml/util.py
index a486c6a3fdeb..4a731e299d1a 100644
--- a/python/pyspark/ml/util.py
+++ b/python/pyspark/ml/util.py
@@ -147,6 +147,23 @@ def overwrite(self):
         return self
 
 
+@inherit_doc
+class GeneralMLWriter(MLWriter):
+    """
+    Utility class that can save ML instances in different formats.
+
+    .. versionadded:: 2.4.0
+    """
+
+    def format(self, source):
+        """
+        Specifies the format of ML export (e.g. "pmml", "internal", or the fully qualified class
+        name for export).
+        """
+        self.source = source
+        return self
+
+
 @inherit_doc
 class JavaMLWriter(MLWriter):
     """
@@ -191,6 +208,24 @@ def session(self, sparkSession):
         return self
 
 
+@inherit_doc
+class GeneralJavaMLWriter(JavaMLWriter):
+    """
+    (Private) Specialization of :py:class:`GeneralMLWriter` for :py:class:`JavaParams` types
+    """
+
+    def __init__(self, instance):
+        super(GeneralJavaMLWriter, self).__init__(instance)
+
+    def format(self, source):
+        """
+        Specifies the format of ML export (e.g. "pmml", "internal", or the fully qualified class
+        name for export).
+        """
+        self._jwrite.format(source)
+        return self
+
+
 @inherit_doc
 class MLWritable(object):
     """
@@ -219,6 +254,17 @@ def write(self):
         return JavaMLWriter(self)
 
 
+@inherit_doc
+class GeneralJavaMLWritable(JavaMLWritable):
+    """
+    (Private) Mixin for ML instances that provide :py:class:`GeneralJavaMLWriter`.
+    """
+
+    def write(self):
+        """Returns an GeneralMLWriter instance for this ML instance."""
+        return GeneralJavaMLWriter(self)
+
+
 @inherit_doc
 class MLReader(BaseReadWrite):
     """