Which statement regarding leave-one-out cross-validation (LOOCV) is correct?

Leave-one-out cross-validation (LOOCV) is indeed a special case of k-fold cross-validation, where the number of folds is equal to the number of observations in the dataset. In LOOCV, for each iteration, one observation is left out as the validation set while the remaining observations are used for training the model. This process is repeated for every observation in the dataset, leading to a model being trained n times (where n is the total number of observations).

This method allows every single observation to be utilized for both training and validation purposes, ensuring that the model is evaluated on nearly all available data, which is particularly beneficial when the dataset is small. The uniqueness of LOOCV as a form of k-fold validation lies in its structure, as it can be viewed as a k-fold process where k equals the sample size.

In contrast, other options touch on concepts related to bias, variance, and the general applicability of LOOCV compared to k-fold validation, but they do not correctly encapsulate the precise relationship that LOOCV maintains as a specific form of k-fold cross-validation. Thus, recognizing that LOOCV constitutes a specific instance of k-fold is essential in understanding its implementation and implications in statistical modeling.