Flax is an important economic crop for seed oil and stem fiber. Phenotyping of traits such as seed yield, seed quality, stem fiber yield, and quality characteristics is expensive and time consuming. Genomic selection(GS) refers to a breeding approach aimed at selecting preferred individuals based on genomic estimated breeding values predicted by a statistical model based on the relationship between phenotypes and genome-wide genetic markers. We evaluated the prediction accuracy of GS(rMP) and the efficiency of GS relative to phenotypic selection(RE) for three GS models: ridge regression best linear unbiased prediction(RR-BLUP),Bayesian LASSO(BL), and Bayesian ridge regression(BRR), for seed yield, oil content, iodine value, linoleic, and linolenic acid content with a full and a common set of genome-wide simple sequence repeat markers in each of three biparental populations. The three GS models generated similar rMPand RE, while BRR displayed a higher coefficient of determination(R~2)of the fitted models than did RR-BLUP or BL. The mean rMPand RE varied for traits with different heritabilities and was affected by the genetic variation of the traits in the populations.GS for seed yield generated a mean RE of 1.52 across populations and marker sets, a value significantly superior to that for direct phenotypic selection. Our empirical results provide the first validation of GS in flax and demonstrate that GS could increase genetic gain per unit time for linseed breeding. Further studies for selection of training populations and markers are warranted. Flax is an important economic crop for seed oil and stem fiber. Phenotyping of traits such as seed yield, seed quality, stem fiber yield, and quality characteristics is expensive and time consuming. preferred individuals based on genomic estimated breeding values ​​predicted by a statistical model based on the relationship between phenotypes and genome-wide genetic markers. We evaluated the prediction accuracy of GS (rMP) and the efficiency of GS relative to phenotypic selection (RE) for three GS models: ridge regression best linear unbiased prediction (RR-BLUP), Bayesian LASSO (BL), and Bayesian ridge regression (BRR) for seed yield, oil content, iodine value, linoleic, and linolenic acid content with a full and a common set of genome-wide simple sequence repeat markers in each of three biparental populations. The three GS models similar rMP and RE, while BRR displayed a higher coefficient of determination (R ~ 2) of the fitted models than did RR-BLUP or BL. The mean rMPand RE varied for traits with different heritabilities and was affected by the genetic variation of the traits in the populations. GS for seed yield generated a mean RE of 1.52 across populations and marker sets Our quantitative results provide the first validation of GS in flax and demonstrate that GS could increase genetic gain per unit time for linseed breeding. Further studies for selection of training populations and markers are warranted.