Best practices for TensorFlow checkpointing include consistent naming conventions, balancing save frequency with performance, coordinating saves in distributed training using tf.distribute.Strategy, automating checkpoint pruning, verifying restores, and clean code encapsulation. Integrate TensorBoard for monitoring.