Graphical models is a class of probabilistic models that utilizes graphs to represent the conditional dependencies between a set of random variables. Nodes in the graph typically correspond to variables, while edges encode the dependencies among them, providing a structured framework for representing, analyzing, and performing inference on complex multivariate probability distributions.