Size of folds in k-fold cross-validation
I think cross-validation is almost always superior to a simple train-test split. The only problem is computation time (for a 5-fold cross validation you have to train your model 5 times).

If your problem allows it, you should always use cross-validation.

The only reason you shouldn't use cross-validation is if your model takes too long to train (e.g. state-of-the-art image recognition networks require weeks of training on GPU clusters).