Bandit algorithms are a set of strategies in machine learning that optimize decision-making by balancing exploration and exploitation in situations where choices must be made sequentially and their outcomes are uncertain. These are especially useful in contexts such as adaptive clinical trials, online advertising, and recommendation systems where maximizing cumulative rewards is paramount.