Select ALL the correct statements.

Training encompasses the process of adjusting a model's parameters to minimize its error on a training dataset. Testing refers to evaluating the model's performance on new, unseen data to gauge its ability to generalize.

The purpose of the validation set is to select the model based on its performance on a separate dataset from the training set.

The purpose of the validation set is to train the model on a small subset of the data and test it on the remaining data.

Suppose you have a dataset with $1000$ examples, and you want to hevaluate the performance of a machine learning model using $5-$fold crossvalidation. Shuffle the dataset randomly, split it into $5$ subsets of $200$ examples each, use each subset for validation $($i$.$e$.,$ testing$)$ once and the remaining subsets for training, and repeat the process $5$ times.