DFS (Depth-First Search) explores as far as possible along each branch before backtracking, while BFS (Breadth-First Search) explores all nodes at the present level before moving to the next level.

Depth-First Search (DFS) and Breadth-First Search (BFS) are two fundamental algorithms for traversing or searching graph data structures. DFS starts at a source node and explores as far as possible along each branch before backtracking, following a 'depthward' approach. DFS can be implemented using a stack, either explicitly or through recursion, and is useful for scenarios like solving mazes or puzzles, where you need to explore one path deeply before trying others. It’s also used in detecting cycles in graphs and for topological sorting in Directed Acyclic Graphs (DAGs). BFS, on the other hand, explores all nodes at the current depth level before moving on to nodes at the next level. BFS can be implemented using a queue, and it’s commonly used in finding the shortest path in unweighted graphs or searching for nodes that are 'closest' to the starting node. Unlike DFS, BFS is often more suited for scenarios where you need to explore the 'breadth' of a problem, such as finding the shortest path in a graph or in algorithms like Dijkstra’s shortest path when all edges have the same weight. While both algorithms have their use cases, the choice between DFS and BFS depends on the problem at hand, the structure of the data, and whether depth or breadth is more important.