Merge sort is a highly efficient sorting algorithm that employs the divide-and-conquer strategy to sort a list or array of elements. The main idea behind merge sort is to recursively divide the input list into smaller sublists until each sublist contains a single element. A list with one element is inherently sorted, providing the base case for the recursion. The algorithm then begins to merge the sublists back together in a sorted manner. The merging process involves comparing the elements of two sorted sublists and arranging them in order until all elements are combined into a single sorted list. This merging step is performed iteratively or recursively, ensuring that the final output is a completely sorted list. The time complexity of merge sort is O(n log n) in all cases (worst, average, and best), making it one of the most efficient sorting algorithms available. Additionally, merge sort is a stable sorting algorithm, meaning that it preserves the relative order of equal elements, which can be beneficial in certain applications. However, merge sort requires additional space for the temporary sublists during the merging process, resulting in a space complexity of O(n). Despite this, merge sort is often preferred for sorting large datasets, especially when stability is required or when working with linked lists. Understanding merge sort and its implementation is essential for mastering sorting algorithms and enhancing problem-solving skills in computer science.