Final answer:
K-fold cross-validation is not also called sliding estimation; the correct statement is false. K-fold cross-validation is a statistical method used for evaluating the performance of a model and involves rotating validation and training subsets.
Step-by-step explanation:
The statement 'K-fold cross-validation is also called sliding estimation' is false. K-fold cross-validation is a model validation technique used for assessing how the results of a statistical analysis will generalize to an independent data set.
It involves dividing a dataset into 'K' equally sized folds or subsets, where one set is retained as the validation data for testing the model, and the remaining 'K-1' sets are used as training data.
The process is repeated 'K' times, with each of the 'K' subsets used exactly once as the validation data. Sliding estimation, on the other hand, is not a recognized term for K-fold cross-validation or a widely used term in statistical modeling.