It’s the optimal low-rank approximation of a matrix in some norm-2 sense, it has those nice beautiful orthogonal factors on both, and it is often quite easy to compute. Why wouldn’t you use it, right? If you are going to have one trick, truncated SVD is a good pick.

Does he like Halko, Martinsson, and Tropp’s randomized SVD? It is pretty slick.