Binary Classification on small dataset < 200 samples
This post might be of interest. Basically by selecting the model with the best crossvalidation score, you already account for overfitting.

Also, you should separate your dataset into two parts. For the first one (validation) you run the crossvalidation on to select a model, in your case LinearSVC. For the second one (testing) you run crossvalidation again, but this time only with LinearSVC to get unbiased estimates of the accuracy.