We discuss error estimates for the well-known Longstaff-Schwartz algorithm, a least-squares Monte Carlo regression algorithm for American/Bermudan option pricing. The overall goal is to establish the most general such estimates, presupposing the fewest hypotheses possible on the hypothesis sets (approximation architectures) used in the regression as well as the underlying distributions. Both the sample and approximation errors are addressed.