Vectorization matters in machine learning because model training is fundamentally an optimization problem: an algorithm such as gradient descent repeatedly updates the model's parameters to reduce prediction error. Implementing these parameter-update calculations as vectorized operations, using optimized linear algebra libraries, instead of explicit element-by-element loops makes each optimization step dramatically faster to compute. Vectorized code is also simpler to write and easier to debug, since more of the underlying arithmetic is delegated to well-tested library routines rather than hand-written loops.