I am a bit confused about the application of cross-validation. So, if I have a big data set, I will split my data into test and training data and and perform validation on the test data. But if I have a small data set I would like to used cross-validation and then the validation is already performed within it.
What puzzles me is that lots of people split data, perform training on training data with cross-validation, and then perform validation on the test dataset. So they combine those two methods. Is this a proper way to do it? May I do only cross-validation since my data set is quite small?