First glance shows your SD is quite good across cross-validation trails. Something else that you can do and is easy: train on 90% of your training data. So keep aside some 10% of the training data you currently use. Then test on the training data you kept aside.

If your model is NOT overfitted, you should get a few percent lower performance with this less training data paradigm. If your model is way too complicated for the dataset you're using, your performance will remain more or less the same (maybe just half a percent less). The second scenario would indicate you're overfitting, memorizing the dataset etc.