The multinomial distribution is a probability distribution that generalizes the binomial distribution to experiments with more than two possible outcomes. It describes the probabilities of obtaining a specific number of counts for multiple categories when each category has a fixed probability of occurrence. In a multinomial experiment, a fixed number of trials are conducted, and the outcomes can fall into one of several categories. This distribution is particularly useful in various fields, including statistics, machine learning, and natural language processing, where it helps model scenarios like word counts in text classification or the results of rolling dice.