What is the advantage of using cross$-$validation over a single training$/$test split?

Group of answer choices

Cross$-$validation is faster to run than a single training$/$test split.

Cross$-$validation requires less data than a single training$/$test split.

Cross$-$validation trains and evaluates several models, giving a more robust measure of performance.

Cross$-$validation uses stratified training$/$validation splits by default, ensuring a balanced representation of all classes in both sets.