In k-fold cross-validation, what happens in each iteration?

Question

BlackTom AI · Accepted Answer

In k-fold cross-validation, the dataset is partitioned into k equal (or near-equal) sized folds, and the model undergoes k training/testing rounds. 
Option 1: 'One fold is used for validation, the other folds for training' describes exactly this process, where in each iteration one fold serves as the validation set and the remaining k-1 folds form the training set. This allows the model to be evaluated on data it hasn't seen during training while being trained on the rest. 
Option 2: 'The model is tested on the full dataset' is inaccurate because testing on the full dataset would not provide a meaningful assessment of generalization, since the model would have been trained on part of that data. 
Option 3: 'Data is randomly generated' is incorrect; k-fold cross-validation uses partitions of the existing data, not new or randomly generated data, to create training and validation splits. 
Option 4: 'All folds are combined at once' misstates the procedure; while predictions from each fold can be aggregated after all iterations, the core process requires evaluating the model on one held-out fold at a time, not simultaneously on all folds during a single iteration.

COMM_V 414 103 2025W1 Concept Check 3 (individual)

In k-fold cross-validation, what happens in each iteration?

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