Why does SVD provide optimal low-rank approximation?

SVD minimizes reconstruction error among all rank-k approximations.

The Eckart-Young theorem states that truncating SVD gives the best possible approximation of a matrix under Frobenius norm. This makes it optimal for compression and noise reduction.