Selective Spur Pruning is a model-level optimization technique in machine learning that iteratively removes less salient weight connections during training to enhance computational efficiency while preserving model accuracy, distinguished by its targeted, criteria-based elimination of specific spurious pathways rather than uniform or random pruning.