Correct: random shuffling lets some folds train on data from after the point in time the validation fold is testing on, implicitly handing the model future information.
Option 1 is the trap this lesson exists to break — k-fold’s safety is conditional on the data actually being exchangeable (order-independent), which time-ordered data isn’t. Option 3 invents a rule that doesn’t exist (k is a practical choice, commonly 5 or 10, not a hard requirement). Option 4 overcorrects — cross-validation is a genuine improvement over a single split when its assumptions hold; the fix here is a different splitting strategy, not abandoning the idea.
Walking the actual failure. Suppose the data spans January through December, shuffled, then split into 5 folds. Fold 3 might, purely by chance, end up validating on a March data point while folds 1, 2, 4, and 5 (used to train that round) happen to include June, September, even December data. A model predicting March sales that got to “see” patterns from June onward during training has information no real deployment would ever have — you can’t train on the future to predict the past. The validation score that round looks good for the wrong reason: not because the model generalizes well, but because it partially memorized what happens later and got tested on an earlier point it’s implicitly already seen the shape of.
The fix: respect the data’s actual structure when splitting, not just its row count. For time-ordered data, a time-series split (also called forward-chaining or rolling-origin CV) trains only on data strictly before each validation fold — fold 1 trains on January, validates on February; fold 2 trains on January–February, validates on March; and so on, always moving forward, never backward. It’s a direct generalization of lesson 2’s single split’s core rule (“test on data the model hasn’t seen”) applied k times instead of once, respecting the one thing plain shuffling ignored: which data existed before which.
The broader principle, worth carrying past this one example: cross-validation estimates generalization only under the assumption that folds are genuinely exchangeable — that any row could just as easily have landed in any fold. Time order breaks that assumption outright; so does having multiple rows from the same underlying entity (the same patient, same user, same store) scattered across folds, which is next lesson’s failure mode. More folds and a fancier-sounding procedure is not automatically more rigorous — it can just be the same leak, dressed up to look more careful.
Where this goes: this lesson zoomed in on the CV-splitting version of leakage. Next lesson generalizes: leakage isn’t only a cross-validation problem — it comes in a handful of recognizable shapes (target, temporal, group) that show up throughout an entire modeling pipeline, not just at the split step.