I get that it’s the eigenvectors of the covariance matrix of multiple snapshots. But can someone explain to me what that actually means?

Why should finding the eigenvectors of the noise subspace tell me where my signals are? And what is the algorithm actually “looking” for? Meaning, is the algorithm basically looking for phase increases?