What is the difference between the validation set and the test set in machine learning?

Group of answer choices

The validation set is an alternative term for the test set, both serving the same purpose in assessing model performance.

The validation set is used for model evaluation during training, while the test set is reserved for the final assessment of the model's generalization performance.

The validation set is used for fine$-$tuning hyperparameters, while the test set is used for training the model.

The validation set is used for assessing the model's generalization, and the test set is used for hyperparameter tuning.