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 on average. These algorithms are particularly useful in fields like computer science, cryptography, and machine learning. For example, the Monte Carlo method is a well-known randomized algorithm that estimates numerical results through random sampling. Overall, randomized algorithms can be powerful tools for solving complex problems efficiently.