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Oats (Avena sativa L.) provide unique nutritional benefits and contribute to sustainable agricultural systems. Breeding high‐value oat varieties that meet milling industry standards is crucial for satisfying the demand for oat‐based food products. Test weight, thins, and groat percentage are primary traits that define oat milling quality and the final price of food‐grade oats. Conventional selection for milling quality is costly and burdensome. Multi‐trait genomic selection (MTGS) combines information from genome‐wide markers and secondary traits genetically correlated with primary traits to predict breeding values of primary traits on candidate breeding lines. MTGS can improve prediction accuracy and significantly accelerate the rate of genetic gain. In this study, we evaluated different MTGS models that used morphometric grain traits to improve prediction accuracy for primary grain quality traits within the constraints of a …