Cross-validation is testing a model several times, on different slices of data, to be sure the score isn't just a lucky break.
Imagine it like this: Instead of taking a single mock exam to know whether you're ready, you take 5 different mock exams covering different chapters, then average your scores. That gives a much fairer picture of what you really know.
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