Markov Chain Monte Carlo (MCMC) is a statistical method used to sample from complex probability distributions. It works by creating a sequence of random samples, where each sample depends only on the previous one, following the Markov property. This allows researchers to explore high-dimensional spaces and approximate distributions that are difficult to calculate directly. The process involves generating a chain of samples that eventually converge to the desired distribution. By using techniques like Metropolis-Hastings or Gibbs sampling, MCMC helps in estimating parameters in various fields, including machine learning, Bayesian statistics, and physics, making it a powerful tool for data analysis.