Expectation Maximization (EM) is an iterative algorithm used to find the maximum likelihood estimates of parameters in statistical models with latent variables. It alternates between estimating the expected value of the latent variables given the current parameters (E-step) and maximizing the likelihood by updating the parameters (M-step).