Feedback on stratified k fold validation

I am doing some applied work with CNNs in the academic world.

I have a relatively small dataset.

I am doing 10 fold stratified cross validation(?) where I do an initial test-train split, and then the data in the train split is further cross validated to a 10 fold train-validate split.

I then run the ensemble of 10 train models against the test split, and I select the results from the best performing model against the test data as the predicted values for the test data.

Is this a reasonable strategy? Thank you!