Which statement regarding cross-validation is true?

When evaluating the statements regarding cross-validation, the first one correctly indicates that performing Leave-One-Out Cross-Validation (LOOCV) necessitates fitting a model a total of n times, where n is the number of observations in the dataset. This is because each iteration involves leaving out one observation for validation while using the rest for training, thus requiring the model to be trained n times.

The second statement is also accurate; in the context of least squares regression and LOOCV, you can estimate the test Mean Squared Error (MSE) effectively after performing the n fits because it allows for an accurate approximation of how the model would perform on unseen data by testing on each individual observation that was left out sequentially.

The third statement is correct as well. K-fold cross-validation divides the dataset into k subsets or "folds." The model is trained k times, with each of the k folds being used once as the validation set while the remaining k-1 folds serve as the training set. This provides a robust evaluation of the model's performance.

Given that all these statements are true, the response affirming that all of the statements are accurate stands validated based on the principles of cross-validation methods in statistical modeling.