Randomized algorithms are computational methods that use random numbers to make decisions during their execution. They can provide approximate solutions to problems more quickly than deterministic algorithms, which follow a fixed sequence of steps. By introducing randomness, these algorithms can often avoid worst-case scenarios and improve performance in practice. These algorithms are particularly useful in fields like computer science, cryptography, and machine learning. Examples include Monte Carlo methods, which rely on random sampling to estimate numerical results, and Las Vegas algorithms, which always produce a correct result but may take varying amounts of time to do so.