Step 1: Understand what BFS (Breadth-First Search) is. It is an algorithm used to explore nodes and edges of a graph level by level.
Step 2: Identify what 'space complexity' means. Space complexity refers to the amount of memory required by an algorithm to run as a function of the size of the input.
Step 3: In BFS, we use a queue to keep track of the nodes (or vertices) that we need to explore next.
Step 4: The maximum number of nodes that can be stored in the queue at any time is equal to the number of vertices (V) in the graph, especially when we reach the last level of the graph.
Step 5: Therefore, the space required for the queue is proportional to the number of vertices, which is O(V).
Step 6: Conclude that the space complexity of BFS is O(V) because of the storage needed for the queue.
