diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearSVCExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearSVCExample.java
index a18ed1d0b48fa..ddca5318b04c7 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearSVCExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearSVCExample.java
@@ -20,6 +20,7 @@
 // $example on$
 import org.apache.spark.ml.classification.LinearSVC;
 import org.apache.spark.ml.classification.LinearSVCModel;
+import org.apache.spark.ml.classification.LinearSVCTrainingSummary;
 import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SparkSession;
@@ -47,6 +48,15 @@ public static void main(String[] args) {
     // Print the coefficients and intercept for LinearSVC
     System.out.println("Coefficients: "
       + lsvcModel.coefficients() + " Intercept: " + lsvcModel.intercept());
+
+    LinearSVCTrainingSummary trainingSummary = lsvcModel.summary();
+    System.out.println("Total Iteration: " + trainingSummary.totalIterations());
+    // Obtain the objective per iteration.
+    double[] objectiveHistory = trainingSummary.objectiveHistory();
+    System.out.println("objectiveHistory:");
+    for (double lossPerIteration : objectiveHistory) {
+      System.out.println(lossPerIteration);
+    }
     // $example off$
 
     spark.stop();
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala
index 38bd048bec559..4561fe3d9dca7 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala
@@ -252,7 +252,7 @@ class LinearSVC @Since("2.2.0") (
     val model = copyValues(new LinearSVCModel(uid, coefficientVector, interceptVector))
     val trainingSummary = LinearSVCTrainingSummary($(labelCol), $(featuresCol),
       objectiveHistory, objectiveHistory.length)
-    model.setSummary(trainingSummary)
+    model.setSummary(Some(trainingSummary))
     instr.logSuccess(model)
     model
   }
@@ -290,15 +290,26 @@ class LinearSVCModel private[classification] (
   @Since("2.2.0")
   def setWeightCol(value: Double): this.type = set(threshold, value)
 
-  private var trainingSummary: LinearSVCTrainingSummary = _
+  private var trainingSummary: Option[LinearSVCTrainingSummary] = None
 
   private[classification]
-  def setSummary(summary: LinearSVCTrainingSummary): this.type = {
+  def setSummary(summary: Option[LinearSVCTrainingSummary]): this.type = {
     this.trainingSummary = summary
     this
   }
 
-  def summary: LinearSVCTrainingSummary = trainingSummary
+  /**
+   * Gets summary of model on training set. An exception is
+   * thrown if `trainingSummary == None`.
+   */
+  @Since("2.2.0")
+  def summary: LinearSVCTrainingSummary = trainingSummary.getOrElse(
+    throw new SparkException("No training summary available for this LinearSVCModel")
+  )
+
+  /** Indicates whether a training summary exists for this model instance. */
+  @Since("2.2.0")
+  def hasSummary: Boolean = trainingSummary.isDefined
 
   private val margin: Vector => Double = (features) => {
     BLAS.dot(features, coefficients) + intercept
@@ -368,7 +379,7 @@ object LinearSVCModel extends MLReadable[LinearSVCModel] {
 }
 
 /**
- * Abstraction for Linear SVC Training results.
+ * Linear SVC Training results.
  * Currently, the training summary ignores the training weights except
  * for the objective trace.
  */
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 77fe162ca11e3..6ab7a6c9b828a 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -172,6 +172,15 @@ def intercept(self):
         """
         return self._call_java("intercept")
 
+    @property
+    @since("2.2.0")
+    def hasSummary(self):
+        """
+        Indicates whether a training summary exists for this model
+        instance.
+        """
+        return self._call_java("hasSummary")
+
     @property
     @since("2.2.0")
     def summary(self):
@@ -179,9 +188,12 @@ def summary(self):
         Gets summary (e.g. objective history, total iterations) of model
         trained on the training set.
         """
-
-        java_blrt_summary = self._call_java("summary")
-        return LinearSVCTrainingSummary(java_blrt_summary)
+        if self.hasSummary:
+            java_blrt_summary = self._call_java("summary")
+            return LinearSVCTrainingSummary(java_blrt_summary)
+        else:
+            raise RuntimeError("No training summary available for this %s" %
+                               self.__class__.__name__)
 
 
 @inherit_doc
@@ -189,7 +201,7 @@ class LinearSVCTrainingSummary(JavaWrapper):
     """
     .. note:: Experimental
 
-    Abstraction for LinearSVC Training results.
+    Linear SVC Training results.
     Currently, the training summary ignores the training weights except
     for the objective trace.