The Expectation-Maximization (EM) Algorithm is an iterative method used to find maximum likelihood estimates of parameters in statistical models with latent variables. It alternates between estimating the expected value of the latent variables (E-step) and maximizing the likelihood function given these expectations (M-step), improving the parameter estimates with each iteration until convergence.