A model validation technique where the data is split into multiple training and test sets to evaluate how well a model will generalize to unseen data. Commonly, k-fold cross-validation divides data into k parts, training on k-1 folds and testing on the remaining fold in rotation. In the context of forex trading, cross-validation helps ensure that a trading model isn’t just overfitting one time period; it estimates expected out-of-sample performance by averaging over multiple data splits.

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