Quasi-Monte Carlo methods are numerical techniques used to approximate integrals and solve high-dimensional problems by employing low-discrepancy sequences instead of random sampling, which enhances convergence rates compared to traditional Monte Carlo methods. These methods are particularly effective in scenarios where higher precision is required, such as in financial modeling and computational physics, due to their ability to reduce variance and improve accuracy with fewer samples.