Questions about model selection using cross-validation
Let's say a dataset was split as training and testing sets. Multiple models were compared using cross-validation on training datasets.
In one scenario, some of models yielded exact same validation errors.
In another scenario, the models accuracy might rank as 99%, 98%, 97%, 95%,90%.......etc.
For these two scenarios, could you please advise how and why to choose a model?
I understand test dataset is just designed to evaluate generalization error. But for the scenarios above, whether it is time to use test dataset to evaluate those models.