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Local search in machine learning refers to a class of optimization algorithms that iteratively explore the solution space to find the optimal or near-optimal solution for a given problem. Unlike global search algorithms that aim to explore the entire solution space, local search methods focus on making incremental improvements to a current solution. 

The basic idea behind local search is to start with an initial solution and iteratively move towards neighboring solutions that have better objective function values. The objective function represents the measure of how good or bad a solution is in the context of the optimization problem. 

Common examples of local search algorithms include hill climbing, simulated annealing, and genetic algorithms. These algorithms are often used in problems where it is impractical to explore the entire solution space due to its vast size, and finding a locally optimal solution is sufficient for the given application. 

Local search is particularly useful in problems with large search spaces, non-linear and complex objective functions, and situations where finding a globally optimal solution is computationally expensive or infeasible. 

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