Recursion allows solving problems by breaking them into smaller, similar subproblems, useful for tasks like tree traversal, backtracking, and dynamic programming.

Understanding recursion is essential in competitive programming because it allows problems to be broken down into smaller, more manageable subproblems, often leading to elegant solutions. Recursion is particularly useful for tasks involving repetitive structures, such as traversing tree-like data or performing operations that require exploration of all possible choices, as seen in backtracking and dynamic programming. For instance, in tree traversal algorithms (preorder, inorder, postorder), recursion simplifies navigation through nodes. In dynamic programming, recursive methods often help structure solutions, with memoization enhancing efficiency by storing results. However, recursion also demands careful handling to avoid issues like stack overflow, which can arise from deep recursive calls in large datasets. Writing recursive functions requires a clear understanding of base cases and recursive cases to prevent infinite loops or excessive memory use. Mastering recursion involves practice and familiarity with various recursive patterns, making it a valuable skill for solving complex problems in competitive programming efficiently and effectively.