Conditional Random Fields (CRFs) are a type of probabilistic graphical model used for structured prediction, where the goal is to predict a sequence of labels for a sequence of input data. Unlike Hidden Markov Models, CRFs model the conditional probability of the label sequence given the input sequence, allowing them to relax independence assumptions and incorporate a wide range of contextual information.