The Baum-Welch Algorithm is a statistical method used to estimate the parameters of a Hidden Markov Model (HMM). It is particularly useful when the model's states are not directly observable, allowing researchers to infer the underlying state sequences from observed data. The algorithm iteratively improves the estimates of transition and emission probabilities based on the observed sequences. This algorithm operates in two main steps: the Expectation step, where it calculates the expected state probabilities given the current parameters, and the Maximization step, where it updates the parameters to maximize the likelihood of the observed data. This process continues until convergence, resulting in optimized model parameters.