Lazy evaluation in Spark allows for data processing operations to be deferred until absolutely necessary, optimizing performance by minimizing unnecessary computations. It creates a logical execution plan, called a DAG, which is only executed when an action is triggered. This approach improves efficiency by eliminating redundant computations and allows for optimization opportunities such as predicate pushdown and column pruning. Lazy evaluation also enables Spark to automatically perform advanced optimizations, like pipelining transformations, and improves fault tolerance by allowing for automatic recovery of lost data.

Lazy evaluation is a key feature of Spark that allows computations to be postponed until the results are actually needed. This has several advantages. First, it allows Spark to optimize the execution plan based on the available data and transformations applied, resulting in more efficient processing. Second, it enables Spark to take advantage of data locality, by scheduling computations close to the data rather than moving data around unnecessarily. Finally, lazy evaluation allows for better fault tolerance, as it allows Spark to recompute lost or corrupted data on the fly, without having to rerun the entire computation.