Final answer:
In k-fold cross-validation, each part of the data is used once for validation and k-1 times for training, as the dataset is split into k equal parts and each is in turn used as a validation set.
Step-by-step explanation:
In k-fold cross-validation, the dataset is divided into k blocks or 'folds'. Each fold acts as the validation set exactly once, and as part of the training set k-1 times.
For example, in a 5-fold cross-validation, the dataset is split into 5 parts. Each part is used for validation one time and for training four times, as during each iteration or 'fold', 1 part is held out for validation while the remaining 4 parts are used for training.
This process is repeated until each of the k folds has served as the validation datasIn k-fold cross-validation, each part of the data is used for training and validation k times. It is called k-fold because the data is divided into k subsets, or folds.
The training and validation process is repeated k times, each time using a different fold as the validation set and the remaining folds as the training set.et.