diff --git a/docs/ml-classification-regression.md b/docs/ml-classification-regression.md
index 782ee581889347a66450111e6a3e6455d3c47e5a..37862f82c3386288828c39ddbb27cd48cf7f10eb 100644
--- a/docs/ml-classification-regression.md
+++ b/docs/ml-classification-regression.md
@@ -363,6 +363,50 @@ Refer to the [R API docs](api/R/spark.mlp.html) for more details.
</div>
+## Linear Support Vector Machine
+
+A [support vector machine](https://en.wikipedia.org/wiki/Support_vector_machine) constructs a hyperplane
+or set of hyperplanes in a high- or infinite-dimensional space, which can be used for classification,
+regression, or other tasks. Intuitively, a good separation is achieved by the hyperplane that has
+the largest distance to the nearest training-data points of any class (so-called functional margin),
+since in general the larger the margin the lower the generalization error of the classifier. LinearSVC
+in Spark ML supports binary classification with linear SVM. Internally, it optimizes the
+[Hinge Loss](https://en.wikipedia.org/wiki/Hinge_loss) using OWLQN optimizer.
+
+
+**Examples**
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classification.LinearSVC) for more details.
+
+{% include_example scala/org/apache/spark/examples/ml/LinearSVCExample.scala %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/LinearSVC.html) for more details.
+
+{% include_example java/org/apache/spark/examples/ml/JavaLinearSVCExample.java %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classification.LinearSVC) for more details.
+
+{% include_example python/ml/linearsvc.py %}
+</div>
+
+<div data-lang="r" markdown="1">
+
+Refer to the [R API docs](api/R/spark.svmLinear.html) for more details.
+
+{% include_example r/ml/svmLinear.R %}
+</div>
+
+</div>
## One-vs-Rest classifier (a.k.a. One-vs-All)
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
new file mode 100644
index 0000000000000000000000000000000000000000..a18ed1d0b48fa7d315ad762be8addcaa8da7ef1b
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaLinearSVCExample.java
@@ -0,0 +1,54 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.ml;
+
+// $example on$
+import org.apache.spark.ml.classification.LinearSVC;
+import org.apache.spark.ml.classification.LinearSVCModel;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SparkSession;
+// $example off$
+
+public class JavaLinearSVCExample {
+ public static void main(String[] args) {
+ SparkSession spark = SparkSession
+ .builder()
+ .appName("JavaLinearSVCExample")
+ .getOrCreate();
+
+ // $example on$
+ // Load training data
+ Dataset<Row> training = spark.read().format("libsvm")
+ .load("data/mllib/sample_libsvm_data.txt");
+
+ LinearSVC lsvc = new LinearSVC()
+ .setMaxIter(10)
+ .setRegParam(0.1);
+
+ // Fit the model
+ LinearSVCModel lsvcModel = lsvc.fit(training);
+
+ // Print the coefficients and intercept for LinearSVC
+ System.out.println("Coefficients: "
+ + lsvcModel.coefficients() + " Intercept: " + lsvcModel.intercept());
+ // $example off$
+
+ spark.stop();
+ }
+}
diff --git a/examples/src/main/python/ml/linearsvc.py b/examples/src/main/python/ml/linearsvc.py
new file mode 100644
index 0000000000000000000000000000000000000000..18cbf87a10695c9ea4a6c5ba6d3fa2010995e765
--- /dev/null
+++ b/examples/src/main/python/ml/linearsvc.py
@@ -0,0 +1,46 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from __future__ import print_function
+
+# $example on$
+from pyspark.ml.classification import LinearSVC
+# $example off$
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+ spark = SparkSession\
+ .builder\
+ .appName("linearSVC Example")\
+ .getOrCreate()
+
+ # $example on$
+ # Load training data
+ training = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
+
+ lsvc = LinearSVC(maxIter=10, regParam=0.1)
+
+ # Fit the model
+ lsvcModel = lsvc.fit(training)
+
+ # Print the coefficients and intercept for linearsSVC
+ print("Coefficients: " + str(lsvcModel.coefficients))
+ print("Intercept: " + str(lsvcModel.intercept))
+
+ # $example off$
+
+ spark.stop()
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/LinearSVCExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/LinearSVCExample.scala
new file mode 100644
index 0000000000000000000000000000000000000000..5f43e65712b5d8634c995a45430c1e7eac9cbe1a
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/LinearSVCExample.scala
@@ -0,0 +1,52 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.ml
+
+// $example on$
+import org.apache.spark.ml.classification.LinearSVC
+// $example off$
+import org.apache.spark.sql.SparkSession
+
+object LinearSVCExample {
+
+ def main(args: Array[String]): Unit = {
+ val spark = SparkSession
+ .builder
+ .appName("LinearSVCExample")
+ .getOrCreate()
+
+ // $example on$
+ // Load training data
+ val training = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
+
+ val lsvc = new LinearSVC()
+ .setMaxIter(10)
+ .setRegParam(0.1)
+
+ // Fit the model
+ val lsvcModel = lsvc.fit(training)
+
+ // Print the coefficients and intercept for linear svc
+ println(s"Coefficients: ${lsvcModel.coefficients} Intercept: ${lsvcModel.intercept}")
+ // $example off$
+
+ spark.stop()
+ }
+}
+// scalastyle:on println