Answer: (iv) All of the above

Correct uses of Cross Validation (CV):

Cross validation (k-fold, LOOCV, stratified) estimates generalization performance by partitioning data into train/validation folds repeatedly.

1. (i) Selecting variables / feature selection: CV error is used to compare subsets of features and choose set with lowest validation error, avoiding overfitting.
2. (ii) Comparing predictors / model selection: Compare different model families (e.g., linear vs tree vs SVM) on CV score.
3. (iii) Electing / selecting parameters in prediction function (hyperparameter tuning): e.g., choosing $k$ in KNN, $\lambda$ in ridge, $C$, $\gamma$ in SVM via grid search with CV. Also called hyperparameter optimization.

Since all three are standard applications, (iv) All of the above is correct.

Additional note: CV does not train final model; after selection, model is retrained on full training data and evaluated on held-out test set.