Recursion is a key concept in programming and DSA (Data Structures and Algorithms), where a function calls itself to solve smaller instances of a larger problem. The idea behind recursion is breaking down a complex problem into smaller, more manageable pieces. This approach is particularly useful when the problem itself has a recursive structure, meaning it can naturally be divided into smaller subproblems that resemble the original. For instance, the Fibonacci sequence is a classic example of recursion. To find the nth Fibonacci number, you can break it down into the sum of the two preceding numbers, which are solved recursively. Similarly, recursion is widely used in tree and graph traversal algorithms like Depth-First Search (DFS). When dealing with hierarchical data like trees, recursion allows you to explore each node and its children systematically. Another classic use case is the Tower of Hanoi problem, where recursive steps are used to move disks between pegs following specific rules. Recursion can make the code cleaner and more intuitive by eliminating the need for explicit loops, but it also has its limitations. Each recursive call adds a new frame to the call stack, which can lead to performance issues like stack overflow if the recursion goes too deep. This is why understanding base cases, where recursion stops, is critical to avoid infinite loops. Some problems, like factorial computation or traversing a file system, are easier to solve using recursion. However, recursive algorithms can often be converted into iterative ones for performance reasons. Dynamic programming techniques, like memoization, can be used alongside recursion to store intermediate results and avoid recomputation, making recursive algorithms more efficient.