Memoization is an optimization technique used in recursive algorithms to avoid redundant calculations, significantly improving efficiency. It involves storing the results of function calls in a data structure, such as a hash table or array, so that repeated calls with the same inputs can return precomputed results. This approach is particularly useful in problems with overlapping subproblems, like Fibonacci sequence calculations or dynamic programming problems such as knapsack or longest common subsequence. Memoization can reduce the time complexity of recursive solutions from exponential to polynomial by ensuring each unique subproblem is solved only once. Understanding how to implement memoization and use it effectively allows you to tackle complex recursive problems that would otherwise be computationally intensive.