Two different ways to rescale a feature — and two different sets of assumptions that make picking the wrong one a real, common mistake.
"Just rescale the features" sounds like one step with one right answer, but it isn't. Normalization (most commonly min-max scaling) and standardization(Z-score scaling) both take a feature's raw values and put them on a comparable scale — but they do it with different formulas, produce outputs with fundamentally different properties, and fail in different ways when a dataset has outliers or a non-normal shape. Treating them as interchangeable synonyms for "scaling" is one of the most common tuning mistakes in applied data science.