The Metropolis-Hastings algorithm is a method used in statistics to sample from complex probability distributions. It helps researchers generate samples that can approximate the distribution of interest, even when direct sampling is difficult. By proposing new sample points and accepting or rejecting them based on a specific probability, the algorithm ensures that the resulting samples reflect the desired distribution. This technique is particularly useful in Bayesian statistics and Markov Chain Monte Carlo (MCMC) methods. It allows for efficient exploration of high-dimensional spaces, making it easier to analyze data and draw conclusions in various fields, including machine learning and physics.