diff --git a/docs/submitting-applications.md b/docs/submitting-applications.md
index 0319859539..402dd0614f 100644
--- a/docs/submitting-applications.md
+++ b/docs/submitting-applications.md
@@ -162,9 +162,10 @@ The master URL passed to Spark can be in one of the following formats:
 <tr><th>Master URL</th><th>Meaning</th></tr>
 <tr><td> <code>local</code> </td><td> Run Spark locally with one worker thread (i.e. no parallelism at all). </td></tr>
 <tr><td> <code>local[K]</code> </td><td> Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine). </td></tr>
-<tr><td> <code>local[K,F]</code> </td><td> Run Spark locally with K worker threads and F maxFailures (see <a href="configuration.html#scheduling">spark.task.maxFailures</a> for an explanation of this variable) </td></tr>
+<tr><td> <code>local[K,F]</code> </td><td> Run Spark locally with K worker threads and F maxFailures (see <a href="configuration.html#scheduling">spark.task.maxFailures</a> for an explanation of this variable). </td></tr>
 <tr><td> <code>local[*]</code> </td><td> Run Spark locally with as many worker threads as logical cores on your machine.</td></tr>
 <tr><td> <code>local[*,F]</code> </td><td> Run Spark locally with as many worker threads as logical cores on your machine and F maxFailures.</td></tr>
+<tr><td> <code>local-cluster[N,C,M]</code> </td><td> Local-cluster mode is only for unit tests. It emulates a distributed cluster in a single JVM with N number of workers, C cores per worker and M MiB of memory per worker.</td></tr>
 <tr><td> <code>spark://HOST:PORT</code> </td><td> Connect to the given <a href="spark-standalone.html">Spark standalone
         cluster</a> master. The port must be whichever one your master is configured to use, which is 7077 by default.
 </td></tr>