Technical Abstract: We intuitively believe that the dramatic drops in the cost of DNA marker information we are currently experiencing should have immediate benefits in accelerating the delivery of crop varieties with improved yield, quality, and biotic and abiotic stress tolerance. But these traits are complex and affected by many genes, each with small effect. Traditional marker assisted selection has been ineffective for such traits. The introduction of genomic selection (GS), however, has shifted that paradigm. Rather than seeking to identify individual loci significantly associated with a trait, GS uses all marker data as predictors of performance and consequently delivers more accurate predictions. Selection can be based on GS predictions, potentially leading to more rapid and lower cost gains from breeding. The objectives of this paper are to review essential aspects of GS and summarize the important take-home messages from recent theoretical, simulation, and empirical studies. We then look forward and consider research needs surrounding methodological questions and the implications of GS for long-term selection.