diff --git a/python/pyspark/ml/linalg/__init__.py b/python/pyspark/ml/linalg/__init__.py
index 9da983667b..f99161c881 100644
--- a/python/pyspark/ml/linalg/__init__.py
+++ b/python/pyspark/ml/linalg/__init__.py
@@ -980,14 +980,14 @@ class DenseMatrix(Matrix):
             return self.values[i + j * self.numRows]
 
     def __eq__(self, other):
-        if (not isinstance(other, DenseMatrix) or
-                self.numRows != other.numRows or
-                self.numCols != other.numCols):
+        if (self.numRows != other.numRows or self.numCols != other.numCols):
             return False
+        if isinstance(other, SparseMatrix):
+            return np.all(self.toArray() == other.toArray())
 
         self_values = np.ravel(self.toArray(), order='F')
         other_values = np.ravel(other.toArray(), order='F')
-        return all(self_values == other_values)
+        return np.all(self_values == other_values)
 
 
 class SparseMatrix(Matrix):
diff --git a/python/pyspark/ml/tests/test_linalg.py b/python/pyspark/ml/tests/test_linalg.py
index 995bc35e4c..0c25e2b818 100644
--- a/python/pyspark/ml/tests/test_linalg.py
+++ b/python/pyspark/ml/tests/test_linalg.py
@@ -112,11 +112,17 @@ class VectorTests(MLlibTestCase):
         v4 = SparseVector(6, [(1, 1.0), (3, 5.5)])
         v5 = DenseVector([0.0, 1.0, 0.0, 2.5])
         v6 = SparseVector(4, [(1, 1.0), (3, 2.5)])
+        dm1 = DenseMatrix(2, 2, [2, 0, 0, 0])
+        sm1 = SparseMatrix(2, 2, [0, 2, 3], [0], [2])
         self.assertEqual(v1, v2)
         self.assertEqual(v1, v3)
         self.assertFalse(v2 == v4)
         self.assertFalse(v1 == v5)
         self.assertFalse(v1 == v6)
+        # this is done as Dense and Sparse matrices can be semantically
+        # equal while still implementing a different __eq__ method
+        self.assertEqual(dm1, sm1)
+        self.assertEqual(sm1, dm1)
 
     def test_equals(self):
         indices = [1, 2, 4]
diff --git a/python/pyspark/mllib/linalg/__init__.py b/python/pyspark/mllib/linalg/__init__.py
index 94a3e2af4d..df411d7990 100644
--- a/python/pyspark/mllib/linalg/__init__.py
+++ b/python/pyspark/mllib/linalg/__init__.py
@@ -1135,14 +1135,14 @@ class DenseMatrix(Matrix):
             return self.values[i + j * self.numRows]
 
     def __eq__(self, other):
-        if (not isinstance(other, DenseMatrix) or
-                self.numRows != other.numRows or
-                self.numCols != other.numCols):
+        if (self.numRows != other.numRows or self.numCols != other.numCols):
             return False
+        if isinstance(other, SparseMatrix):
+            return np.all(self.toArray() == other.toArray())
 
         self_values = np.ravel(self.toArray(), order='F')
         other_values = np.ravel(other.toArray(), order='F')
-        return all(self_values == other_values)
+        return np.all(self_values == other_values)
 
 
 class SparseMatrix(Matrix):
diff --git a/python/pyspark/mllib/tests/test_linalg.py b/python/pyspark/mllib/tests/test_linalg.py
index f26e28d174..703aed2fe1 100644
--- a/python/pyspark/mllib/tests/test_linalg.py
+++ b/python/pyspark/mllib/tests/test_linalg.py
@@ -115,11 +115,17 @@ class VectorTests(MLlibTestCase):
         v4 = SparseVector(6, [(1, 1.0), (3, 5.5)])
         v5 = DenseVector([0.0, 1.0, 0.0, 2.5])
         v6 = SparseVector(4, [(1, 1.0), (3, 2.5)])
+        dm1 = DenseMatrix(2, 2, [2, 0, 0, 0])
+        sm1 = SparseMatrix(2, 2, [0, 2, 3], [0], [2])
         self.assertEqual(v1, v2)
         self.assertEqual(v1, v3)
         self.assertFalse(v2 == v4)
         self.assertFalse(v1 == v5)
         self.assertFalse(v1 == v6)
+        # this is done as Dense and Sparse matrices can be semantically
+        # equal while still implementing a different __eq__ method
+        self.assertEqual(dm1, sm1)
+        self.assertEqual(sm1, dm1)
 
     def test_equals(self):
         indices = [1, 2, 4]