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In machine learning and search algorithms, uninformed search refers to a search strategy that doesn't have any additional information about the problem beyond the definition of the problem itself. In other words, the algorithm doesn't use any domain-specific knowledge to guide its search through the solution space. Uninformed search algorithms are also known as blind search algorithms because they explore the search space without any knowledge of where the goal might be located. 

The most common uninformed search algorithm is the depth-first search (DFS) and breadth-first search (BFS). These algorithms systematically explore the possible solutions without considering the characteristics of the problem domain. While uninformed search algorithms are straightforward and easy to implement, they may not be efficient in situations where additional information about the problem is available. 

In contrast, informed search algorithms, also known as heuristic search algorithms, use domain-specific knowledge to guide the search and make more informed decisions about which paths to explore first. This often leads to more efficient and effective search processes. 

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