I'm new to data science and I have a problem understanding what dataset to use when using cross validation for model evaluation.
Let's say I have two models: LogisticRegression and RandomForestClassifier. After I train them on training set I want to have an idea which one is better. If I want to do cross validation, do I do it on the training set, or the whole dataset (training and testing)? I have seen some contradicting methods.