I guess you are asking for something slightly different here: after you used cross validation (CV) for model training, evaluation, and selection, you train the best performing model type and hyperparameter set once again, using all training data. This model is the one you will use on your held back test set on once. So if I understand correctly what you want to point out is that only using CV and getting some results does not change anything in the training process of the chosen model, hence conceptually does not prevent any overfitting - and this is correct so far. But:
Your CV results give you more and better information about how different model types and hyperparameterizations perform on data the model has not seen before. Imagine only doing one training and one cross validation partition: you could be lucky (having the partitions separated in a way that from each you can predict the other well). If you use e.g. a 10 fold CV with 20 repeats you train your model 200 times and each time evaluate it on a portion of data it has not seen before - so this will be much more robust, as being lucky 200 times is less likely. If you have 200 such evaluation results for multiple types of models and multiple hyperparameter sets you have better information on which to choose the model you actually want to train once using all training data. This process ensures you choose something that will less likely overfit, therefore is a big advantage over using a single training+test partition - and I guess this is the difference you asked for in your question.