Mixture models are probabilistic models that represent the presence of subpopulations within an overall population, allowing for the modeling of complex distributions by combining multiple simpler distributions. They are commonly used in clustering, density estimation, and pattern recognition, where the data is assumed to be generated from a mixture of several underlying probability distributions.