Node2Vec is an algorithm designed for scalable feature learning in networks by optimizing a biased random walk procedure to efficiently explore diverse neighborhoods of a node. It extends traditional word embedding techniques to networked data, allowing for the generation of vector representations that capture both local and global structural information of nodes in a graph.