Conditional Random Fields (CRFs) are a class of statistical modeling methods used for structured prediction, particularly in sequence data, where they model the conditional probability of a label sequence given an observation sequence. Unlike Hidden Markov Models, CRFs are discriminative models that do not assume independence between observations, allowing them to capture complex dependencies in the data.