Monte Carlo methods are a class of computational algorithms that utilize repeated random sampling to obtain numerical results. These methods are particularly useful for estimating unknown parameters in complex systems where analytical solutions may be difficult or impossible to derive. By simulating a large number of random samples, Monte Carlo methods can provide approximations for various problems, including integration, optimization, and probabilistic modeling. Their versatility makes them applicable in fields such as finance, engineering, and data science, where they help in making informed decisions based on statistical analysis and simulations.