Expectation-Maximization (EM) is a statistical technique used for finding maximum likelihood estimates of parameters in models with latent variables. It operates in two main steps: the Expectation step (E-step), where the algorithm estimates the missing data based on current parameter estimates, and the Maximization step (M-step), where it updates the parameters to maximize the likelihood of the observed data given the estimated missing data. EM is particularly useful in scenarios like clustering and Gaussian Mixture Models, where the data may have hidden structures. By iteratively refining the estimates, EM converges to a set of parameters that best explain the observed data, even when some information is incomplete.