Checkpointing is essential for fault tolerance in Spark Structured Streaming applications with stateful operations. It allows the application to recover from failures by periodically storing state information. The best strategy involves configuring checkpointing to a reliable and fault-tolerant storage system like HDFS or a cloud-based storage system. This ensures the safety of state information even during node failures. Additionally, carefully selecting the checkpoint interval balances the performance overhead with recovery time, optimizing both fault tolerance and performance efficiency. Options A, C, and D either compromise fault tolerance or performance, making them less suitable for production environments.