Randomized complexity classes are a categorization of decision problems based on the resources needed by randomized algorithms to solve them, often considering time or space complexity with a probability of error. These classes help in understanding the power and limitations of randomness in computational processes, offering insights into problems where deterministic solutions are inefficient or unknown.