When I search for the answer online, it seems like there is a disagreement on what cross-validation is. Some say k-fold cross validation is used to get an estimate of how well a model will perform before building the model on all of the data. See below:
Some say k-fold cross validation is used on the training set of data to obtain an optimal model before applying the model to the testing set. If this way of k-fold cross validation is correct, how is the optimal instance of the model picked? k-fold cross validation will create k instances of the model, so what would be the way to pick one to use on the testing set to get an accuracy measurement.
Thanks in advance.